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基于多忠实神经网络的高合金 (FeNiCoCrCu) 的抗拉强度预测,使用分子动力学数据
Nazib E Elahi Khan Chowdhury1, Alif Jawad1, Anfalur Rahman1
1Department of Mechanical Engineering, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh.
Journal of molecular modeling
|July 22, 2025
概括
本研究引入了一种多忠实度物理信息神经网络 (MPINN) 模型,以准确预测高合金 (HEA) 的抗拉强度,特别是FeNiCoCrCu,降低与传统方法相关的计算成本.
科学领域:
- 材料科学 材料科学 材料科学
- 计算材料科学科学 计算材料科学
- 合金设计设计 合金设计
背景情况:
- 高合金 (HEAs),如FeNiCoCrCu,具有特殊的性能,但在理解和优化其复杂结构方面存在挑战.
- 分子动力学 (MD) 模拟对于原子尺度分析是有价值的,但对于大型系统来说是计算密集的.
- 机器学习 (ML) 为高效和准确的材料属性预测提供了一个有希望的替代方案.
研究的目的:
- 开发和应用多忠实物理信息神经网络 (MPINN) 模型,用于预测FeNiCoCrCu HEA的抗拉强度.
- 为了生成各种FeNiCoCrCu组合的抗拉强度的综合数据集,以训练MPINN模型.
- 为了验证MD数据启用MPINN模型在准确预测材料属性的有效性.
主要方法:
- 使用LAMMPS进行分子动力学 (MD) 模拟和TensorFlow用于机器学习模型开发.
- 由单晶和多晶的MD模拟生成的低保真 (LF) 和高保真 (HF) 数据.
- 采用了Atomsk来创建系统,EAM潜在的力场,OVITO用于可视化;MPINN结合了线性/非线性关系,Adam优化器和L2规范化.
主要成果:
- 通过使用大量FeNiCoCrCu拉伸强度的数据集,成功训练了一个MPINN模型.
- MPINN模型证明了对不同合金组成的抗拉强度的准确预测.
- 验证了MD数据启用MPINN方法用于预测材料属性的有效性.
结论:
- 开发的MPINN模型提供了一种有效和准确的方法来预测FeNiCoCrCu HEA的抗拉强度.
- 与大型系统的传统MD模拟相比,这种方法显著降低了计算负担.
- 该研究强调了基于物理学的神经网络在加速材料发现和优化方面的潜力.
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